46 results about "Aggregate function" patented technology

The invention discloses a private network dynamic access control method and system, and relates to the technical field of network security and access control. The method comprises the following steps: acquiring equipment behavior data and network flow data in a private network, performing feature construction, and generating equipment trust features and network behavior features; and carrying out multi-dimensional risk assessment model training based on the equipment trust features and the network behavior features, carrying out real-time risk assessment on access requests or entities in the private network through the trained multi-dimensional risk assessment model, and generating a real-time risk score through a weighted aggregation function. According to the invention, through the multi-modal feature fusion model based on an attention mechanism, deep association of static attributes and dynamic behavior features of equipment is realized, and a real-time risk score is generated in combination with a multi-dimensional risk assessment model and a weighted aggregation algorithm. The limitation that in traditional access control, the evaluation dimension is single, the static strategy lags behind, and dynamic threats cannot be reflected is effectively overcome, and the accuracy and interpretability of private network access risk perception are remarkably improved.

The invention discloses a federated backdoor defense method based on decoupling contrast learning, and the method comprises the steps: training a backdoor model based on a backdoor sample, and immediately stopping training after the backdoor model converges on the backdoor sample; respectively extracting a penultimate layer vector of the backdoor model and the local model from a sample pair held by the malicious client as a backdoor feature and a clean feature; comparing and learning the separated back door features and the clean features, and learning the clean features for the local model by using a sample weighting strategy to train the local model to obtain a trained local model; and sending local model parameters of the trained local model to a global server, and generating model parameters of a new global model based on the local model parameters through an aggregation function. The method aims at reducing information dependence between backdoor features and clean features through comparative learning, so that local model learning is free of backdoor representation, and the robustness of a global model is improved.

The invention relates to the technical field of warehouse logistics management and artificial intelligence, in particular to a warehouse logistics dynamic scheduling optimization method and system based on deep learning, and the method comprises the steps: collecting static map data and dynamic sensor data of a warehouse environment; constructing a dynamic space-time diagram state at the current moment; in response to a business logic rule of the warehouse management system, triggering a scheduling optimization process, which comprises the following steps: generating a preliminary scheduling intention set by a constraint reinforcement learning scheduler; predicting to obtain a future space-time congestion thermodynamic diagram through a space-time diagram attention network predictor; quantitative processing is carried out through a self-defined aggregation function, and the future congestion cost of the scalar is determined; correcting the preliminary scheduling intention set by constraining a Lagrange solving framework of a reinforcement learning scheduler, and generating a final scheduling intention; outputting the final scheduling intention to a warehousing control system for execution; according to the method, the problem that decision making and prediction are disjointed in traditional scheduling is solved, and conversion from lagging response to active avoidance is achieved.

The present application relates to the technical field of database, provide a kind of Hash aggregation method and device based on Limit statement in openGuass, the method of the present application, comprising: according to query SQL statement generation Limit operator and HashAgg operator, wherein, Limit operator includes limit value, HashAgg operator includes aggregation function and grouping field information;Limit value in Limit operator is passed and saved to HashAgg operator;Execute query SQL, calculate the hash value of grouping field in data line, and construct HashBucket hash bucket according to the non-repeated number of occurrence of the hash value of grouping field and limit value;The aggregated field in the data of the same grouping field with the same hash value is placed in the same HashBucket hash bucket corresponding;After data scanning is completed, the aggregated field in each HashBucket hash bucket is aggregated by aggregation function, and the aggregation result is output to client.The present application can reduce the grouping and aggregation time of data in database and resource consumption as a whole.

The invention discloses a lane line smoothing processing method, device and equipment, a medium and a program product. The method comprises the following steps: determining a fitting position of a position point of a to-be-processed lane line, wherein the fitting position is represented by a plurality of fitting parameters; according to the fitting position, determining a shape loss function between a fitting line represented by the fitting position of the position point and the to-be-processed lane line; according to the position offset between the fitting position of the position point and the original position of the position point in the to-be-processed lane line, determining a flexible constraint condition corresponding to the position point; determining a parameter aggregation function of the flexible constraint parameter corresponding to the to-be-processed lane line based on the flexible constraint parameter corresponding to the position point of the to-be-processed lane line; and based on the flexible constraint condition, solving by combining the shape loss function and the parameter aggregation function to obtain the parameter value of the fitting parameter so as to obtain the smoothed lane line represented by the parameter value. According to the invention, the accuracy of smoothing the lane line can be improved.

The application relates to the technical field of software test automation, and provides a requirement-driven test case automatic generation method and system based on a semantic test graph, which comprises the following steps: performing multidimensional static analysis on source code, respectively constructing an abstract syntax tree, a control flow graph and a function call graph, and extracting function call relations and data flow dependency relations; constructing a semantic test graph by taking functions as nodes, taking function call relations as main edges and taking data flow dependency relations as auxiliary edges, and aggregating function nodes in the semantic test graph into several function modules; obtaining several test requirement nodes, calculating semantic similarity between the test requirement nodes and the function modules, establishing a mapping relationship from the test requirement nodes to the function modules, and forming a requirement-program structure association graph; and based on the requirement-program structure association graph, generating a test intention for each test requirement node, and converting the test intention into an executable test case. The highly corresponding relationship between the generated test case and the business requirement is ensured.

The invention relates to the technical field of deep learning, and particularly discloses a neural network solving method and system for a weighted maximum satisfiability problem, and the method comprises the steps: representing an input conjunctive normal form formula as an edge splitting factor graph; constructing a graph neural network model, wherein the model comprises a supervised message passing layer and an unsupervised solution enhancement layer; an edge splitting factor graph is input to the graph neural network model, in the message passing layer, message passing is conducted on four types of edges through independent aggregation functions, and initial assignment prediction of variables is generated; and inputting the initial assignment prediction into the unsupervised solution enhancement layer, evaluating the quality of the current assignment, and carrying out iterative optimization on the variable assignment based on a relaxation optimization technology so as to output an optimized final solution. The invention provides a new graph representation method for a CNF formula, namely an edge splitting factor graph. According to the method, the spanning tree is generated, richer structure information is provided for the GNN, and therefore the learning efficiency is improved in the message passing process.

The embodiment of the application provides a kind of power load prediction method and device based on structure perception aggregation function, its method includes: obtaining the node feature of each node in the power system topology model and the node feature of its neighbor node, and based on structure perception aggregation function, the node feature of each node in the power system topology model and the node feature of its neighbor node are aggregated, to obtain the node feature after aggregation of each node;Obtain the historical time series feature of each node in the power system topology model, and by fusing the historical time series feature of each node with the aggregated node feature, obtain the fusion feature data of each node;By inputting the fusion feature data of each node into the trained deep neural network prediction model, the power load prediction result of each node is obtained.

Embodiments of the present application provide a data updating method, a data querying method, a device and a storage medium. The method comprises the following steps: receiving a write request for a database table; and updating the database table and a first index thereof according to the write request and a preset aggregation function, wherein the first index is used to store a pre-aggregation result of pre-aggregating records of the database table by using the aggregation function. The detection efficiency of the detection scheme provided by the embodiments of the present application is high. In the technical scheme provided by the embodiments of the present application, only an index for storing the pre-aggregation result of the database table needs to be created for the database table, and real-time consistency between the database table and the pre-aggregation result thereof can be realized. Compared with the prior art, the scheme is simple and easy to implement.

The technology is generally directed to protecting data privacy in query results. In response to a query, data responsive to the query results is identified. The responsive data can include rows of data. The rows of data are aggregated based on an aggregation function. A function or hash of the aggregated values may be used to generate a signature associated with the rows of data within the query results. The signatures of the current query may be compared to other signatures associated with the current query results and / or historical query result signatures, e.g., signatures associated with results of previously submitted queries. If the similarity of the signature associated with the current query result is above a threshold similarity to another signature, the query and its associated results are identified as a privacy attack. Preventative actions may be enabled in response to determining that the query is a privacy attack.

The application relates to a method and system for improving the speed of writing shared main memory critical resources in parallel by a new generation Shenwei many-core processor, which comprises the following steps: a slave core applies for a data space on its private local data memory; critical resource data in the main memory is copied to the respective private local data memory; each slave core performs read-write operation; each slave core initiates a reduction operation through a remote memory access (RMA) channel, wherein the reduction operation refers to performing certain aggregation function operation on the critical resource data in the private local data memory of the plurality of slave cores to obtain a final result; and the critical resource data in the private local data memory after the reduction operation is written back to the main memory through a direct memory access (DMA) channel. The method can effectively improve the speed of reading and writing the shared main memory critical resources by the slave core of the Shenwei many-core processor, and improve the performance and efficiency of the supercomputer.

The application discloses a binary program vulnerability detection method based on fine-tuning of a pre-trained model, which divides instructions in function-level assembly code into branch and control class instructions, data class instructions and other class instructions according to the role of assembly instructions in the semantic reservation of control flow and data flow, and under the constraint of a preset maximum Token length threshold, selects the above three types of instructions in turn according to a ladder priority, and then reorders and flattens them according to original line number indexes to obtain a simplified one-dimensional assembly Token sequence. The method further adopts a partial freezing fine-tuning strategy to adapt the pre-trained encoding model to a task, and combines a double-layer bidirectional gated recurrent unit network and a multi-head attention aggregation mechanism with a mask to model the semantics and aggregate function-level features of function-level assembly code samples, and finally outputs a vulnerability classification result by a classifier. The application can effectively improve the performance of function-level binary program vulnerability detection.

This invention discloses a method and system for local differential privacy aggregate value data aggregation based on sketches and sampling, belonging to the field of privacy computing and big data analysis technology. The method includes: initializing and disclosing sketch structure parameters on the server side; constructing aggregate value data on the user side, randomly selecting counter indices and determining the hash collision set; randomizing the hash collision set elements on the user side according to the aggregation function type, calculating the hit rate and performing adaptive pruning; reporting triples on the user side; and accumulating and updating the sketch counter on the server side, calculating the aggregated statistics through median estimation. This invention replaces traditional multi-element perturbation with sketch sampling and lightweight reporting, without requiring additional privacy budget consumption, effectively solving the noise accumulation problem, significantly reducing computational and communication overhead, and achieving a balance between privacy protection and statistical utility.

This disclosure provides a database index generation method and apparatus, a data processing method and apparatus, relating to the field of computer technology, specifically to distributed systems, storage management, and other technical fields. The specific implementation scheme is as follows: based on the metadata of the metadata database in the distributed file system, determine the items to be aggregated related to file quotas; based on the items to be aggregated, determine the item aggregation function; create an aggregation index in the metadata database; perform statistics on the items to be aggregated according to the item aggregation function to obtain statistical data; and store the statistical data as aggregated data in the aggregation index.

The invention provides a database query statement optimization method, a medium, a product and equipment. The method comprises the following steps: acquiring and analyzing a database query statement; judging whether the database query statement contains a partition window function and an aggregation function at the same time or not; under the condition that the database query statement simultaneously comprises a partition window function and an aggregation function, judging whether a partition reference column of the partition window function has a uniqueness constraint and a non-null constraint or not; under the condition that the partition reference column has the unique constraint and the non-empty constraint, determining the aggregation type of the aggregation function; and equivalently rewriting the database query statement according to the aggregation type to obtain an optimized query statement. Through the method, redundant window operation and aggregation operation in the database query statement can be reduced, so that the query performance of the database is improved.

The invention discloses an electric appliance circuit overload risk intelligent analysis method based on a graph neural network, and the method comprises the following steps: S1, collecting the operation data of an electric appliance circuit, and constructing an electric appliance circuit diagram containing node and edge attributes; s2, generating a structural representation of each node by adopting an E-GraphSAGE model; s3, generating state representation based on the time sequence data; s4, calculating the difference between the structure representation and the state representation, and generating a semantic conflict score; s5, a semantic conflict score is introduced to construct a conflict perception aggregation function, and conflict representation is generated; s6, splicing the three types of representations to generate a fusion representation; s7, inputting the fusion representation into a risk prediction module to generate a risk tag; s8, extracting high-risk nodes and constructing a conflict path diagram; and S9, outputting a risk label, fusing the representation and the conflict path diagram. According to the invention, the graph structure and state information are fused, and the electric appliance line overload risk identification capability is improved.

This disclosure provides a method and system for knowledge graph completion that integrates relation description and relation propagation, relating to the field of knowledge graph completion technology. The method includes: acquiring relation description text information and inputting it into a language model BERT to extract description vectors of all relations, forming a relation matrix; combining entity nodes and the relation matrix, aggregating neighbor edges using an aggregation function to obtain contextual propagation messages about the set of node neighbor edges; repeating the aggregation and update functions multiple times to obtain the final contextual relation information; constructing a relation path from the relation type sequence of all edges in the original path; extracting the embedded representation of the contextual relation information and the embedded representation of the relation path; fusing the embedded representation of the contextual relation information and the embedded representation of the relation path; using the fused embedded representation to predict relation distribution and determine the relative position of the relation path, thus completing the completion of missing relations in the knowledge graph.

The invention discloses a natural resource dynamic monitoring method based on deep learning, and relates to the technical field of natural resource monitoring, and the method comprises the steps: dividing a target region into a basic geographic unit, periodically updating a monitoring demand index, and grading the monitoring demand index into a core unit, an important unit and a conventional unit; differentiated scene pre-training is carried out for the monitoring priority and task characteristics of each unit, resource investment is accurately matched, and the contradiction between precision and cost and the contradiction between comprehensiveness and pertinence in traditional monitoring are effectively balanced. Meanwhile, each unit serves as a node, multi-dimensional feature vectors are extracted, edge connection is judged in combination with spatial association and attribute similarity, weights are given, deep feature fusion is achieved through an exclusive aggregation function and an association conduction mechanism, spatial linkage and time continuity of dynamic changes of natural resources are captured, one-sidedness of local analysis is avoided, and the accuracy of feature fusion is improved. And the integrity and reliability of monitoring results are improved.

The invention discloses an implementation method for aggregating bitwise OR operation in Spark data processing. The implementation method comprises the following steps: S1, defining a UDAF (Unified Data Adaptive Function) through programming; s2, registering the UDAF defined in the S1, registering a UDAF aggregation bitwise or operation function, generating an execution plan, and applying the registered UDAF to a Spark SQL (Structured Query Language); and S3, sending the execution plan generated in the S2 to a task execution process, and initializing the task and executing the task plan by the task execution process. According to the method, a Spark custom aggregation bitwise or operation technology can be realized, various complex business requirements can be flexibly processed in Spark through custom bitwise or aggregation functions, the efficiency and accuracy of data analysis are improved, and people can easily cope with various challenges and changes.

This invention discloses a conversational business query method and system based on permission semantic parsing and dynamic desensitization backtracking. The method receives multiple rounds of business queries from users; parses user identity, position, organizational level, authorized data domain, field sensitivity level, and display granularity into permission semantic vectors, and aligns them with a query semantic graph to form a permission envelope; generates candidate logical queries based on the query semantic graph, and constructs a fragment backtracking key for each answer fragment, including field source, aggregation function, filter condition hash value, permission version, desensitization rule version, minimum display granularity, and session inheritance tag; when there is follow-up questioning, error correction, drill-down, or permission changes, the fragment backtracking key is compared and the permission residual change is calculated, driving the fragment security state machine to execute valid, pending recalculation, frozen, downgraded, or rejected display. This method can improve the security, traceability, and interactive continuity of business query results.

A method, computer program product, and computing system for processing a continuous query including an aggregating function. An accumulator associated with the aggregating function is identified. A current aggregation result is generated for the aggregating function of the continuous query without accessing every entry of the accumulator. A continuous query result is generated for the continuous query using the accumulator associated with the aggregating function.

The present application relates to warehouse logistics management and artificial intelligence technical field, specifically for warehouse logistics dynamic scheduling optimization method and system based on deep learning, including: collecting static map data and dynamic sensor data of warehouse environment; constructing dynamic space-time graph state of current time; in response to the business logic rules of warehouse management system, triggering scheduling optimization process, including: generating preliminary scheduling intent set by constraint reinforcement learning scheduler; through space-time graph attention network predictor, the future space-time congestion heat map is predicted; through the quantitative processing of the self-defined aggregation function, the future congestion cost of the scalar is determined; through the Lagrange solution framework of the constraint reinforcement learning scheduler, the preliminary scheduling intent set is corrected, and the final scheduling intent is generated; output the final scheduling intent to the warehouse control system for execution; the present application solves the problem that decision and prediction are disconnected in traditional scheduling, and realizes the change from lag response to active avoidance.

The application relates to the technical field of deep learning recommendation, in particular to a multi-criterion recommendation method and system based on graph representation learning. The method comprises the following steps: constructing a bipartite graph set of an interaction graph; generating a sampled subgraph through a graph sampling strategy, and obtaining a new bipartite graph set for training; initializing graph embedding of nodes in each bipartite graph in the new bipartite graph set; aggregating local embedding and global embedding of each node in the sampled subgraph of each criterion through an aggregation function, and generating embedding representation of each node at each layer under each auxiliary criterion; obtaining embedding representation of each node at each layer under the target criterion according to a multi-head attention mechanism, and further obtaining the final embedding representation of each user and product under the target criterion; updating all parameters to be learned in the model according to an optimization target; and calculating the interaction probability of the user and the product, and generating the order of recommended products for the user. The application improves the training efficiency and precision.

A method, computer program product, and computing system for processing a continuous query including an aggregating function. An accumulator associated with the aggregating function is identified. A current aggregation result is generated for the aggregating function of the continuous query without accessing every entry of the accumulator. A continuous query result is generated for the continuous query using the accumulator associated with the aggregating function.

The application provides a method and system for automatic proof of SQL equivalence based on linear integer algebra, comprising: a plan generation step of parsing an input SQL query statement to generate an original logical plan; an ORDER BY processing step of processing an ORDER BY sorting operation in the original logical plan to generate a new logical plan without the ORDER BY sorting operation, which is submitted to a subsequent step for equivalence verification; a U-expression generation step of parsing the logical plan to generate a corresponding algebraic expression U-expression; a normalization step of normalizing and simplifying the U-expression to generate a standard form U-expression; a LIA * conversion step of generating a LIA * expression according to the standard form U-expression; a first-order logic expression generation step of generating a LIA expression according to the LIA * expression, and performing automatic verification of SQL equivalence by means of an SMT solver. The application perfects modeling of SQL features such as an aggregation function, and significantly enhances the capability of automatic verification of SQL equivalence.

Techniques are disclosed for enhancing the transparency and interpretability of machine learning (ML) models using explainable artificial intelligence (XAI). In some embodiments, a computing system generates an XAI model that provides reasons for the outputs of a first ML model by selecting from a set of predefined reasons based on an aggregation function. This aggregation function combines importance scores for various features associated with the ML model's output, where each feature is mapped to a corresponding reason. The computing system may determine one or more parameters for the aggregation function to improve the accuracy of the selected reason, allowing for adjustments in how the aggregation function processes the importance scores. In certain cases, the system may involve an imitation model that is trained to replicate the first ML model's outputs.

A method may include obtaining input data including an environmental condition and chemical properties of input components of an input fluid mixture, encoding, by an encoder machine learning model, the input data to obtain encoded input data, and receiving, by an aggregator function and from the encoder machine learning model, the encoded input data ordered in a sequence corresponding to an order of the input components. The method may further include aggregating, by the aggregator function, the encoded input data to obtain aggregated input data. The aggregated input data may be independent of the sequence. The method may further include decoding, by a decoder machine learning model, the aggregated input data to obtain output data including a phase for an output mixture, and presenting the output data.

The embodiment of the invention provides a data processing method and device, computer equipment and a readable storage medium. The method comprises the steps that S function calls meeting replacement conditions are obtained from client program codes of an application client; n function calls meeting the aggregation condition and S-N function calls not meeting the aggregation condition are obtained from the S function calls, the N function calls are aggregated into aggregated function calls, and a relocation function corresponding to the target function signature is generated according to the aggregated function calls and the S-N function calls; respectively replacing the S function calls with relocation function calls to obtain replaced first package program codes and replaced sub-package program codes; and writing a relocation function in the replaced first package program code to obtain a target first package program code. According to the invention, the compiling memory of the application client can be reduced, and the stability of the application client is improved.

The application discloses a time sequence database statistical information preprocessing method, an electronic device and a storage medium, belongs to the database technical field, and aims to solve the technical problem of how to reduce invalid performance loss caused in the data transmission process, and ensure that the size of data reading and the system overhead of aggregation function calculation can be reduced during aggregation query. The technical scheme is that a preprocessing statistical file is added to the storage layer, a corresponding preprocessing statistical file is added to each table in the storage engine, the preprocessing statistical file saves the statistical information of each column data in the device in the form of a fixed time period, and is updated in real time during data insertion; when the data in the table is aggregated and calculated, the aggregation function and the query time period are pushed down to the storage engine of the storage layer, and the storage engine finds the statistical information of the corresponding time period from the preprocessing statistical file according to the time range of the query.